This paper provides an accessible methodology for mapping audience-constructed genres using the online image-sharing platform Instagram. We apply the method to classify artists who utilize public space in relation to the categories ‘street art’ and ‘graffiti bombing’ based on correlations between an artist’s Instagram follower audience and general ‘street art’ and ‘graffiti bombing’ accounts. By measuring the artist’s audience at different times, we can map not only their specific audience composition but also project their demographic trajectory. Finally we provide a methodology to estimate the total online audience for a specific genre: how many total Instagram accounts might follow street art content? This methodology can function as a powerful analytical tool, but is also easy to use, even for a researcher with limited mathematical or programming experience.

Modern graffiti has a number of points of origin, beginning with its emergence as a distinct genre in the mid-nineteenth century. From the late 1960s, street writing became popular amongst inner-city youth in Philadelphia and New York and by the early 1970s, tags such “Taki 183” were prevalent throughout the boroughs of New York. In 1971 the New York Times ran a story on the phenomenon, titled “Taki 183 spawns pen pals” (Charles, 1971), which helped spread graffiti writing to other cities. Eventually, graffiti’s stylistic elements evolved from simple tags executed with markers to intricate, large-scale letter-based murals or “masterpieces” painted with spray paint. The inclusion of graffiti in music video clips and books in the early 1980s and its relationship with the broader cultural movement of Hip Hop helped it spread across the globe. In its international phase, graffiti retained many of the elements of its New York origins, while developing both as oppositional subcultures in urban centres and expanding into the mainstream, for example through commissioned murals and digital platforms (Deitch, et al., 2011).

In the late twentieth century, the broadening forms of illegal art in shared public spaces was given a new designation: street art. Although it sometimes included forms of graffiti, or work by artists with origins in graffiti culture, street art was also a distinct category. Instead of ornamentalised letters, street art focussed on images, often with a political edge. It also brought new production techniques such as stencil art, but also images glued with wheat paste or printed onto stickers. Much street art draws its central imagery from the public sphere through appropriation, pastiche and assemblage and so re-stages familiar motifs from mainstream culture (for example retro-gaming aesthetics, celebrity faces and advertising icons) (Wacławek, 2011; CDH, 2013a)

Thus, street art and graffiti are cultural forms that are both closely related and yet distinct. On the one hand, the terms graffiti and street art are often used interchangeably and share some common characteristics, such as the shared materials and methods, locations and related institutions, such as magazines and galleries (MacDowall, 2014). In contexts where the terms are opposed, the meaning of the opposition or conflict between them is often organised in relation to broader cultural formations, such as age and generational affiliation, race, class or gender differences. Hence, the opposition between the two forms can be conceived as ideological in nature, serving the interests of various groups, such as councils and state agencies or artists themselves. However, many of the most popular and successful artists have hybrid practices which span both graffiti and street art, have roots in one form (typically graffiti) before moving to another or have parallel practices.

One domain that is often used to differentiate between graffiti and street art is their relationship to mainstream audiences and broad cultural appeal. For example, street art’s use of familiar images and icons gives it a broad cultural legibility, which legitimises street art’s presence within the city aesthetic. By contrast, in its subcultural strand, the inscriptions of graffiti subcultures are often illegible to mainstream culture. Like other subcultures, the oppositional architecture of graffiti requires highly selective admission, that is closed to mainstream audiences (Hebdige, 1979): it has strict terms of membership predicated on gender and a willingness to assume risk; it enacts cultural rituals of youth masculinity; it’s non-commercial; it derives power from the ability to shock through symbolic representation (Macdonald, 2001).

As Bourdieu (1984) argues, cultural legibility is essential to appreciation and hence to legitimacy. Culture that is illegible to the hegemony becomes redefined as ‘lower’, ‘vulgar’ and ‘coarse’ and hence inhibits social mobility:

“A work of art has meaning and interest only for someone who possesses the cultural competence, that is, the code into which it is encoded ... . A beholder who lacks the specific code feels lost in a chaos of sounds and rhythms, colours and lines, without rhyme or reason.”

Normalised discursive positions therefore organise street art and graffiti bombing within a series of binary oppositions that separate the legitimate from the illegitimate: image vs. scrawl, art vs. vandalism, pro-social vs. anti-social. So although street art may have emerged from graffiti culture and both may inscribe public space illicitly, the two cultures have since diverged and the boundaries have become aggressively contested, both within the cultures and from outside (CDH, 2013b; MacDowall, 2014).

Florida (2004) argues that in the shift from industrial to post-industrial economies, innovation and economic growth increasingly come from knowledge-based professionals (for example designers, Web developers and engineers). Under this framework, attracting these knowledge workers to a city becomes essential for growth within the creative economy. Florida argues that these workers are largely drawn by lifestyle factors such as entertainment, cultural diversity and tolerance. Schacter (2015) has argued that street art has been commandeered within this creative city doctrine by providing an ‘edgy’ but safe urban experience to attract creative professionals, while displacing the original community from transitional city spaces. In a more traditional city management policy, many municipal councils now commission street art murals as a deterrence to graffiti (Craw, et al., 2006; Halsey and Pederick, 2010). Both management policies offer civic endorsement to street art while continuing to define graffiti marking as deviant, illegitimate and illegal, which can exacerbate tensions between the two practices. When councils erase graffiti markings (either to re-mark the wall grey or with street art) or endorse other inscriptions by leaving them un-erased, they enforce value judgements about which marks can be seen but also who can mark public space (Honig, 2016) which can create spatial conflicts over place-identity (Dixon and Durrheim, 2000; Cresswell, 2004). The legislative frameworks which denote legitimate and illegitimate use of public space reflect broader contestations of citizenship, belonging and the right to the city.

So there are a number of groups who seek to define the terms ‘street art’ and ‘graffiti’: city councils, street artists and graffiti subcultural practitioners and their audiences. The tension within the classification is reflected thematically in the work of Lush (Figure 1) and prompts a series of simple question: What is street art? What is graffiti? What methodology may be used to classify individual artists or works?

Figure 1: An artwork posted by @Lushsux on Instagram (2015); a physical recreation of the Futurama Fry meme. The image thematically reflects the tension in the classification of ‘street art’ and ‘graffiti’. Reproduced with the permission of the artist.

These questions have been previously considered (Riggle, 2010) and framed within the internal meaning of the artwork; a marking is street art only if it uses the street (in the broad meaning of the urban context) as a material resource. Like many scholars who have addressed this issue, Riggle’s approach is grounded in analytical philosophy and so seeks shared intrinsic qualities of the artwork, tested by reference to a range of examples and thought experiments. However, in this paper we are proposing a different approach, one which assumes that the definitions and boundaries of cultural formations shift over time and that these classifications are contested and fragmented and come to be defined through their usage.

In this paper, we use data available through the online image sharing platform Instagram coupled with genre theory (Altman, 1984), a branch of critical theory, to address these classifications. The experimental methodology is designed to be simple to use and easily accessible to humanities researchers who may have minimal programming experience.

Constructing genre

What methods can be used to describe and map genres? One method is to may employ a textual analysis, interrogating either the semantics or the syntax of the artwork itself (Altman, 1984). For Altman, a text’s semiotic elements constitute the “common traits, attitudes, characters, shots, locations, sets and the like” where the syntactic organisation represents the “constitutive relationships between undesignated and variable placeholders”, in essence the overarching structure of the work. A semantic approach to genre would thus emphasise the fundamental building blocks of the work, where a syntactic analysis focuses on the structures in which the building blocks are arranged.

For example, what makes a Western film distinct from a samurai film? Semantically, Westerns may feature ten-gallon hats and six shooters, where a samurai film may contain feudal robes and swords, but syntactically a Western and a samurai film could be classified within the same genre. For example, Sergio Leone was famously and successfully sued by Akira Kurosawa’s studio for plagiarism; Leone’s classic Western film A fistful of dollars (1964) was an unofficial remake of Kurosawa’s samurai film Yojimbo (1961). Both films feature a protagonist who plays two rival gangs against each other. Other traditional themes and narrative structures also match with Kurosawa’s film, although none of the semantics are repeated (shot locations, weaponry, sets, spoken language etc).

A similar analysis could easily be applied to the genres of street art and graffiti. Semantically, street art typically features mass-media motifs, where graffiti is traditionally letter-based. Syntactically, street is increasingly (although not always) legally sanctioned, while graffiti is typically illegal. Conceivably we could create a list of aesthetic or industrial criteria for the two categories but ultimately this reduces the discussion to a taxonomical accounting of personal subjective criteria. In this paper, we seek a more robust and adaptable methodology to illuminate street art and graffiti genres and their shifts over time. Rather than considering graffiti and street art as coherent aesthtico-political practices, this approach reduces them to a set of mobile and flexible signs of aesthetic gestures that appear on a range of urban locations (from billboards and clothing to Web sites and movie sets).

An alternative to constructing genre from the content of the text is to construct genre by audience response. A film genre can be defined by the target audience (for example a children’s film or an adult film) or by the way the audience responds to the film (for example cult film or a blockbuster). Here cultural consumers define the genre, rather than the text itself: genre is defined through the use of the text rather than the thematic content. Instead of saying an artwork is ‘street art’ if it’s illicit and image-based (or any other criteria) we can define a work as ‘street art’ if it has a large audience of street art aficionados (in the same way that a teen film could be defined by a primarily teenage audience). Audience constructed genre can therefore be considered a consensus classification; a survey of the audience of consumers. The definition and enforcement of the genre then merely reflects the collated individual decisions of the entire audience at a given time.

This type of analysis is attractive to us because it charts perpetually evolving cultural tastes. Ultimately what is interesting is not an absolute, immutable taxonomy of genre but rather the shifts in categorization. We are interested in tools that can be used to study the reasons why audiences identify and distinguish genres at different times.

Genres are open categories that are inherently fluid and unstable (Cohen, 1986). The addition and removal of works reshapes the genre, which leads Cohen to regard genre as a historical process rather than a determinate category; they arise from the human need to construct interrelation and distinction and react against previous classifications. In the early 2000s, a street artwork may have been widely considered vandalism, where today it may be regarded as art. In online space, the term graffiti is used, with increasing regularity, to describe legal image-based murals. Since the 1980s, the term bombing has been used along with other slang terms to describe prolific tagging with an emphasis on damage and anti-aesthetic marking. Language is inherently mutable. Here we offer a tool to help map these shifting landscapes.

Instagram

Goals

Launched in October 2010, Instagram is a free social networking service for mobile phones that allows for image-sharing and video-sharing (up to 15-second clips). By 2015, Instagram had become one of the most widely used social media platforms, with over 400 million users who post media items and follow other users’ accounts. Instagram’s focus on image-based content and the ability to connect with large audiences, means it has been widely adopted in street art and graffiti cultures (MacDowall, 2016).

Rather than merely functioning as a delivery mechanism for the art, digital platforms increasingly shaped the street art produced (MacDowall, 2016, 2008). For example in Figure 1, by directly referencing the Futurama Fry meme (a recurring online image in which titles are added to a popular cartoon figure) Lush adopts a symbolic motif of the online space. Few viewers will encounter the physical painting; it has been specifically manufactured for an online audience and uses the symbolic language of online space.

Instagram offers a number of advantages to an academic researcher studying cultural trends: the rapid growth of Instagram demonstrates the ways in which cultural consumption is increasingly performed online and Instagram data is easily accessible to academic researchers. In our data sets, about 75 percent of Instagram users leave their profiles set to public, which makes all their content and social engagement publically available (this value fluctuates by audience; graffiti oriented pages tend to have higher percentages of private followers). However the platform also has a number limitations to researchers: for example, Instagram users represent a subset of the ‘public’ that is heavily weighted in favour of younger Western audiences; Instagram has in excess of 400 million accounts, but an unknown proportion of these accounts are automatically generated bot accounts; within Instagram different users engage with the platform to varying degrees.

At the time of writing this paper, every item posted on Instagram is shared on the home feed of all the account’s followers, which makes it distinct from other platforms like Facebook. Within the “attention economy”, this means that followers to an Instagram page constitute a commercial asset (Goldhaber, 1997). As a method of broadcast, Instagram allows for the direct exchange of social capital for other modes of capital. For example, many celebrities and athletes are paid to endorse products to their Instagram followers. But the rapidly growing rate of Instagram (both in overall users and the average number of accounts followed by a user) mean this is a rapidly depreciating asset.

Cultural mapping

Bourdieu (1984) argued that cultural taste (for food, clothing, music etc.) is strongly correlated with level of education and social class. Bourdieu employs a correspondence analysis to map cultural taste. He charts the preference for particular cultural products (for example music or art) against the education or income of French citizens or their parents to show that taste becomes an important example of cultural hegemony and ensures the cultural and social reproduction of a societal hierarchy.

Bourdieu determined cultural preferences though direct personal interviews, but the emerging field of cultural analytics allows for the mapping of the data of cultural consumption. Cultural maps can be reproduced more quickly and easily but also with many more orders of magnitudes of respondents (Manovich, 2016).

We are interested in mapping patterns within Instagram audiences, using the data generated by users following other accounts. Instagram “followers” represents a particular type of audience, theoretically distinct from both empirical behaviour (users may never view in detail accounts which they follow), attention (users may ‘follow’ accounts but pay little attention, or alternatively may have a strong interest but not see the content due to the continual churn of Instagram’s feeds) or cultural preference (users may follow accounts which they do not like in order to monitor content, mark social affiliation or due to commercial inducements) (MacDowall, et al., 2015). However, with these caveats, the mapping of followers still remain useful for charting broad cultural preferences and for comparative analysis between Instagram accounts.

How many followers do two accounts have in common? A high number of matches between two accounts may imply they have a similar appeal. Therefore we require audience information from Instagram (specifically the followers for a particular account, defined by a unique user ID number) as opposed to other information that might be used to map cultural taste (images or hashtag data).

We use an online crawler service (MagiMetrics) to collect publicly available user data from Instagram; we download the user names and Instagram identification number of all the followers for particular accounts. Accounts set to private only hide personal information, but their user ID and account name remain publically available and associated as a follower to a particular account. Private accounts are therefore still logged within our data sets. We can then search for matches between accounts (ie users who have liked two profiles) to determine a correlation factor between accounts.

We count the matches in Instagram identification numbers between two user lists, typically up to several hundred thousand users long. This process is repeated for 11 accounts of a selection of popular street artists and graffiti writers. This operation can take several hours to compile on a standard PC. It is possible to count matches between larger list sizes, but the corresponding processing demands increase significantly.

We begin by selecting a series of representative accounts dedicated exclusively to street art or graffiti bombing. For example, the street art accounts all contain the term ‘street art’ either within the account name or in the Instagram profile description and do not contain the term ‘graffiti bombing’. We also limit for accounts over 2,000 users to ensure they are representative of the preferences of the Instagram user base, rather than a network of people who may know each other outside of Instagram. Here the researcher also applies a subjective judgement by approving Instagram profiles as existing within the genre, based on the images contained within the account. We found seven accounts that reasonably represented ‘graffiti bombing’ within these criteria but found that the analysis produces very similar results with fewer accounts. To make full use of our data set, we use all seven accounts in the analysis. We select seven street art accounts which meet the criteria listed above. There are more than seven accounts dedicated to street art on Instagram, but we choose to match the number of accounts to the graffiti bombing account list for internal consistency. The selection of these ‘street art’ and ‘graffiti bombing’ accounts are essential to the analysis because they define the reference frame of the cultural map. A list of the accounts selected and their respective audience size (as of September 2015) is shown in Table 1.

Table 1: List of street art accounts and graffiti bombing accounts (with number of followers) used to construct the genre lists.The seven accounts in each genre define the axes of the cultural plot. Selecting different accounts would change the reference frame of the analysis. The correlation factor plots are only relative to the followers of these seven accounts.

Street art accounts

Graffiti bombing accounts

Account name

Followers

Account name

Followers

@Impermanent_Art

17.4K

@Bombing_City

3.0K

@Street_Art

15.1K

@BombingSP

7.0K

@Street_Art_Hunter

7.7K

@BombingTheSystem

5.5K

@StreetArt_Official

43.6K

@graffbombs

22.6K

@StreetArtCommunity

9.0K

@Graffiti_power

3.8K

@StreetArtFiles

32.1K

@GraffitiFightClub

22.0K

@Urban_StreetArt

5.4K

@TrainBombing

2.0K

Total

130.5K

Total

66.2K

Total unique users

114.7K

Total unique users

58.6K

Users can follow multiple accounts but as we are interested in tracking users (not followings) we remove duplicates from the lists. We are left with a list of 114.7K unique users who constitute a street art audience and 58.6K unique users who represent a graffiti bombing audience.

We can now search for matches between a particular artist and the two audience lists. For example, Banksy is a U.K.-based stencil artist who has 571.1K followers on Instagram (as of September 2015); 3.3K of these followers also appear on the bombing list and 11.1K appear on the street art list. For a reader unfamiliar with programming, the number of matches between two columns of unique data can be calculated simply in Microsoft Excel using the code =sumproduct(countif(ColA, ColB)) where ColA and ColB represent the cells containing the two data series (here the Banksy follower list and the street art audience list).

Two accounts may have a high number of matches because they strongly correlate or because they both simply have large audiences. So we normalise by the audience sizes to calculate a correlation factor. The formula we use for correlation factor is given by:

(1)

Where:

CF: Correlation factor between the artist and genre list (street art or bombing)m: The number of matches between the artist follower list and the genre listSA: The size of the artist follower listSGL: The size of the genre list

So the correlation factor between Banksy and the street art list would be 0.0019. The correlation factor between Banksy and the graffiti bombing list would be 0.00033.

By constructing a Cartesian plot of bombing correlation factor vs. street art correlation factor (Figure 2), we can begin to situate artists with this cultural map. The plot is shown on a log-log axis.

Figure 2: Street art correlation factor plotted against graffiti bombing correlation factor with individual artist accounts mapped to the space. The data is plotted on a log-log scale. Four quadrants are defined to mark regions of high and low correlation with street art and graffiti bombing audiences. Data collected in September 2015.

Within our analysis, the term ‘street art’ now means a high correlation factor with the street art seven accounts listed in Table 1. It could be argued that these accounts have been selected subjectively; it is important to stress that these accounts have been used to define the reference frame for the cultural map (Figure 2) to situate artists for comparison. Different accounts could have been selected, which would change the reference frame of the cultural map. Strictly, the axes of Figure 2 should not be labelled ‘street art correlation factor’ and ‘bombing correlation factor’ but the correlation to user lists in Table 1.

The street art and bombing genre lists are not uniquely independent. The audiences overlap and so there are 3.8K users who are common to both lists. This can be used to calculate a correlation factor between the street art list and graffiti bombing list, providing a correlation factor of 0.00214. This internal correlation factor between the genres can be placed on the cultural map to define four quadrants: regions of high and low correlation with street art and bombing. For example, if an artist has a bombing correlation factor below 0.00214, it would mean that (after normalising for audience sizes) they have fewer followers in common with the bombing audience than street art has in common with the bombing audience — they are less ‘bombing’ than the street art genre itself.

We have placed a selection of 11 street artist/graffiti bomber Instagram accounts onto this cultural map. The processing time required to chart each account makes it unfeasible to map all subcultural practitioners. Instead we have selected Instagram accounts we thought would give a diverse spread of results across the chart, as a tool for validating the methodology. For example, based on the content they produce and the anticipated audience we might expect, it would seem unusual if SaberAWR, an artist known primarlily as a graffiti practitioner had a higher street art correlation factor than Rone, a well-known street artist and may imply a problem with the methodology. We also chose to select artists involved in practices peripheral to street art and graffiti. For example, Selina Miles and The Grifters are primarily focused on digital videography of graffiti. We included a number of Australian artists, simply because as Australian based researchers, we are more familiar with their art practice. The 11 accounts selected are:

@1UP_crew_official: One United Power, a European-based graffiti crew who predominantly paint trains illicitly (93.0K followers)
@Banksy: A U.K.-based satirical stencil artist (571.1K followers)
@HanksyNYC: A New York-based pun stencil artist, and homage to Banksy (75.2K followers)
@Kaws: Pop artist and graphic designer who has often worked on the street (384.3K followers)
@LushSux: Melbourne-based artist known for both letter based tags and image based works (51.6K followers)
@ObeyGiant: the account of Shepard Fairey, a well-known designer and muralist (500.0K followers)
@R_O_N_E: Melbourne-based artist Rone, widely known for painting images of women’s faces (58.0K followers)
@SaberAWR: Los Angeles-based artist known originally for graffiti art (55.7K followers)
@SelinaMiles: Videographer primarily known for making the Limitless YouTube videos with artist Sofles which has over 10 million views (17.0K followers)
@SwoonHQ: Caledonia Curry, a prominent street artist who initially became famous for her wheat-pasted prints (71.0K followers)
@TheGrifters: European-based videographers, who primarily document train painting (31.3K followers)

Returning now to the central thematic tension in the work of Lush (Figure 1), how does this data help us answer the question of whether Lush is primarily a graffiti writer or a street artist? Lush has a prolific career as a bomber and his tags can be found throughout the inner city of Melbourne. However Lush also has a significant online presence creating image-based works (like the image depicted in Figure 1) and regularly sells work through Melbourne street art galleries. Using the theoretical frameworks developed previously (Riggle, 2010) or a semantic textual analysis, the classification is uncertain. Perhaps we would say the artist spans two genres. However using the audience genre analysis discussed above, the result is clearly defined — Lush’s audience overwhelmingly correlated with graffiti bombing accounts. He has only a minor overlap with the street art audience. Audience affiliation makes it more appropriate to group Lush with train painters and traditional graffiti writers, rather than conventional street artists. Looking at the correlation factor directly between Lush and other artists, we find Lush has a low correlation with Swoon (0.00248) or Hanksy (0.00118) but extremely high correlations with 1UP crew (0.0422) or The Grifters (0.0481); of the 31.3K followers of The Grifters, 8.8K (28%) also follow Lush.

Banksy’s work is typically cited as the quintessential example of street art. However a surprising claim now emerges from the data: Banksy is not a street artist. Banksy’s correlation with the street art category is lower than bombing’s correlation with the street art category. So within this audience analysis, it’s more appropriate to classify Banksy within a separate genre. He exists in a category of non-belonging, outside of either street art or bombing. Most of his constituency are not interested in these practices. We could attribute this to his large mainstream recognition, which means his follower base is primarily interested in “Banksy”, not “street art” more broadly. Similarly, Kaws and Hanksy occupy a category of mutual exclusion, although not necessarily for the same reasons.

Banksy and ObeyGiant (Shepard Fairey) function as a good point of comparison; after JR (754K followers) and MrBrainwash (634K followers) they represent the third and fourth most popular ‘street art’ accounts (571K and 500K followers respectively). But they occupy drastically different spaces within the cultural map: ObeyGiant correlates highly with both street art and bombing categories where Banksy correlates with neither. The structure of their accounts are also vastly different: @ObeyGiant has posted 1,507 online media items (photos and clips, April 2016), where @Banksy has posted just 34 (April 2016). The Banksy account functions as an Instagram place holder where the ObeyGiant account is engaged, cultivated and cross-promoted with other artists and celebrities like Stephen Colbert, a television personality. @ObeyGiant funnels traffic to @ObeyClothing, the retail branch of the brand or to the online store on obeygiant.com. In this sense the ObeyGiant Instagram account functions much more like a professional brand account, similar to Nike, to be understood within a broader ecology of digital platforms. In the Instagram space, the ObeyGiant account has worked much harder to accumulate its followers than the Banksy account.

The artist loci depicted in the cultural map in Figure 2 are not static, but are perpetually moving. As an example, imagine a yachting regatta. Taking a snapshot from above reveals the location of each boat (x and y coordinates). Taking a second snapshot at a later time can allow us to plot not only location, but speed and direction as well (a vector field). Our analysis (Figure 2) was first performed on 21 September 2015. We repeated the analysis three months later, on 23 December 2015 and then again on 8 April 2016. This allows us to plot the demographic trajectories of the artists.

Boats plot their own course, but can also be moved by ocean currents that shift the entire field. The analogy applies here too: artists produce content which shapes their demographic trajectories, but they are also susceptible to larger cultural movements (such as shifting interest in bombing and street art). These broader cultural shifts can act on the entire field of artists.

We took the same seven accounts for street art and graffiti bombing and recalculated the unique audience list, as before. The @street_art and @bombingSP accounts were discontinued between December 2015 and April 2016 and so we used the audience list from December for these accounts in April 2016 data. The final audience sizes of unique followers are summarised in Table 2.

Table 2: Rate of growth of graffiti bombing, street art and the matches between the two genre lists, over the interim between data measurements.Note: Three months (September–December 2015) and four months (December 2015–April 2016).

Unique audience size

September 15

December 15

April 16

Graffiti bombing

58,609

69,790 (+19.1%)

81,059 (+16.1%)

Street art

114,728

154,547 (+34.7%)

221,805 (+43.6%)

Matches (street art and graffiti bombing)

3,791

5,652 (+49.1%)

8,498 (+50.4%)

The percentage of matches between the two genre lists had increased more significantly than the total audience sizes. So the correlation factor between the graffiti bombing list and street art list has increased: from 0.002138 in September 2015, to 0.002962 in December 2015 to 0.00402 in April 2016. This implies that the audience distinction between the two genres is diminishing. The audience for graffiti bombing and street art is still growing, but at a rate slower than the rate of interconnection between the two genres. Consumers once exclusively interested in street art are expanding their interest to graffiti bombing and vice versa, perhaps because the aesthetic distinction between the two forms is also diminishing, or due to the hybrid and parallel practices mentioned earlier (MacDowall, 2016).

Another way to frame the result is that graffiti bombing and street art are beginning to stagnate; growth is not coming from new users, but from existing users following more pages. Amongst the users following the seven street art and graffiti bombing accounts, the average number of pages followed amongst the seven has increased in both cases.

Instagram continues to grow and the average number of accounts followed by Instagram users also continues to increase. We can perhaps extrapolate this out to a theoretical endpoint for Instagram; a conceptual limit when everyone on the globe has signed up and followed all other accounts. In our analysis, this would represent complete cultural homogeneity. All accounts would occupy the limit at the top right corner of Figure 2 and all correlation factors would be 1.0.

How will this greater correlation between street art and graffiti bombing affect our cultural map? If the distinction between street art and graffiti bombing is gradually diminishing and the average audience member increasingly follows both cultural practices, we would expect most artists to plot demographical trajectories towards the street art and bombing quadrant; artist audiences will increasingly come from both genre lists. This is a vector pointing towards the conceptual limit of complete cultural homogenisation; a correlation factor co-ordinate of [1.0, 1.0]. In essence, every account on Instagram is ultimately being drawn towards this limit as they collect new followers with more diverse interests.

Figure 3 shows the demographic trajectories for each artist; the correlation factors in September 2015 (black), in December 2015 (red) and April 2016 (blue). The internal correlation factor between street art and graffiti bombing has increased from 0.00214 in September 2015 (black, solid line) to 0.00296 in December 2015 (red, dotted line) to 0.00402 in April 2016 (blue, dashed line). This is consistent with the prediction above; all artist accounts are being dragged towards the top right of the map (increasing correlation with both street art and graffiti bombing). This further supports the conclusion that the two genre audiences are increasingly overlapping.

Figure 3: Demographic trajectories for various artist accounts. The correlation factor coordinates are calculated in September 2015 (black) December 2015 (red) and April 2016 (blue).

To control for the broad cultural shift towards audience homogenisation, we replot the data in Figure 3, but normalise by the internal correlation factor between street art and graffiti bombing (the values 0.00214 (September 2015), 0.00296 (December 2015) 0.00402 (April 2016)). This collapses the black, red and blue axes in Figure 3 to a single fixed reference frame and facilitates better visualisation of the artist’s individual demographic trajectories; imagine the boat race data where the overall ocean current has been cancelled out. When using the normalised correlation, values above 1.0 now represent high correlation and values below 1.0 represent low correlation.

Figure 4: Correlation factor plots that have been normalised by the internal correlation factor between graffiti bombing and street art; this suppresses the effect of broader cultural shifts, which may obscure the individual demographic trajectory of the artist. The graph is plotted on a log-log scale. Data is shown from September 2015 (black), December 2015 (red) and April 2016 (blue).

Of the 11 artists accounts plotted in the cultural map, eight show a clear drift to significantly lower graffiti bombing correlation factors (HanksyNYC, Banksy, Kaws, Rone, ObeyGiant, SelinaMiles, SaberAWR and LushSux). Their growth has come from an audience with significantly reduced interest in graffiti bombing. Alongside the broader convergence between graffiti and street art, we speculate that this reflects a more localised divergence between street art and graffiti bombing practices: as the artists professionalise their careers and become increasingly identified as ‘street artists’ they have diminished interest to a graffiti bombing audience. We identify the observation but we cannot offer a more comprehensive explanation for the result.

Within the seven month duration, Lush posted 289 new media items which were predominantly meme-inspired works (including Figure 1). This represents a departure from the previous graffiti themes of his work: bombing on traditional landscape painting, self-referential graffiti and painting on women’s bodies. So Lush’s shift in demographic composition can easily be reconciled with the new thematic direction of his works. In September 2015 he was featured in Banksy’s satirical theme-park installation, Dismaland. In April 2016 he painted a series of murals of Kim Kardashian, which received widespread mainstream media attention. The rapid audience growth that followed the media attention of both of these events possibly contributes to his diminished correlation factor with graffiti bombing — Lush’s account had moved the greatest distance of our chosen accounts over this period.

The trajectory of The Grifters is also interesting: they are the only account to move to a higher bombing correlation factor. Forty-one percent of their growth between September and December 2015 matches with the graffiti bombing list, although there is no obvious cross-promotion between accounts or by other bombing accounts. They posted 104 new media items between September and December 2015: primarily train bombing and satirical cartoons (by @eewvn). From December 2015 to April 2016, the account focused on promoting works by Felipe Pantone and Taps and Moses for upcoming gallery exhibitions. During this period, their demographic composition within our chart remained unchanged.

A final minor observation is that accounts with higher bombing correlation factors tend to have higher rates of ‘private’ followers (Table 3). This result is reasonable given a heightened concern over criminal prosecution and a more conflict-oriented discussion forum within the graffiti bombing subculture.

Table 3: Average number of accounts followed of the seven accounts used to define ‘street art’ and ‘graffiti bombing’.

Average similar accounts followed

September 15

December 15

April 16

Graffiti bombing

1.129

1.154

1.171

Street art

1.137

1.160

1.200

Estimating total audience size

Our analysis of the actual audience composition of these accounts raised the question of their potential maximum audience size. How large is the Instagram audience for street art and bombing? Repurposing the analysis we can make an order of magnitude estimate of the total audience size.

Let S1 represent the number of followers of a specific street art account (like @streetart_official) and ST represent the total audience size; the total number of Instagram users that might follow any street art content (a number as yet unknown, but that we intend to calculate).

A new user joins Instagram and is interested in consuming street art content. Due to the open nature of the Instagram architecture, they might come across street art content in a variety of ways, for example, through material in their feed from existing followed accounts, through searching hashtags, or by liking certain images, which would result in similar content or accounts being inserted into their search page or feed by the Instagram algorithm.

Will they follow a general street art account, say @streetart_official? If we assume the probability that they follow this general street art account can be expressed as the ratio of audience sizes, the probability can be written as S1/ST.

What is the probability that they also follow a second specific street art account? Assuming there is no correlation between account 1 and account 2 (by cross-promoting accounts for example), the probabilities multiply to become:

(2)

Where:

S1: Audience follower size of account 1S2: Audience follower size of account 2ST: Total audience sizem: Number of matches between the two accounts.

We can measure S1, S2 and m and so it is trivial to rearrange the formula to derive an equation for the total audience size in terms of known parameters:

(3)

To conceptualise this procedure in another way, if the number of matches between two ostensibly identical accounts is very small, this implies the total audience size is very large. If the number of matches between two accounts is close to their total size, it implies both accounts are close to reaching the maximum total audience size (assuming no other correlation between the accounts).

The value ST should not be taken as an absolute audience size, but rather as an effective size, that can be used as a guide to compare between genres.

To estimate the bombing audience, we use the seven accounts used to define the ‘bombing’ audience list. To estimate the street art audience, we also use the seven accounts that define the ‘street art’ audience list, but add the accounts @streetartglobe and @streetartnews. Although there are other Instagram accounts dedicated to street art and bombing, we discount profiles with less than 2,000 followers; they add a large static error (increased standard deviation on repeat calculation) to the audience size estimate.

The large standard deviation on repeat measurements means the values are only useful as an order of magnitude estimate; as might be expected, the audience boundary is very diffuse. In both cases, the total audience size could easily be double or half. But the data implies the Instagram audience for image-based street art muralism is approximately an order of magnitude larger than the audience for graffiti bombing.

The total audience size for street art and bombing are time dependent. As the Instagram user base grows, the maximum audience size also grows.

The total audience size estimate can serve as a rough guide to approximate a maximum follower base for an artist, given the audience composition currently consuming their content. For example, bombing accounts like 1UP crew, Lush and The Grifters have currently grown to about 50 percent, 25 percent and 15 percent of their maximum possible size. If they attract new followers beyond the bombing audience, clearly the accounts can surpass this cap. This would also move the accounts to a lower bombing correlation factor on the cultural map (eventually into the category of non-belonging). So these approximations only hold while the account remains at its current location within the cultural map.

Accounts located in the street art quadrant could grow organically up to approximately 1.6M followers. Although the content shared on these general street art accounts, (particularly the largest, like @StreetArtGlobe) tends to favour large-scale muralism and meme-style puns. So the audience caps may be lower for many of these artists.

Within the current analysis, we cannot draw any conclusions about maximum audiences for accounts in the category of non-belonging (Banksy, Kaws, Hanksy); we have collected little information about the preferences and interests of these artists’ audiences (although it would be straight forward to do a similar type of analysis on these audiences).

We estimate the total street art audience to be 1.3 × 106 (±1.6 × 106 standard deviation) but the Instagram account @StreetArtGlobe has 1.75 × 106 followers. This is within our standard deviation and implies the account has grown to close to its maximum follower base at this time.

Known problems

There are a number of known problems with this analysis, some of which have been discussed earlier. In general, although the economics of social media platforms are complex and vary across the different phases of their development, platforms such as Instagram are subject to internal and external pressures and agents attempting to profit from content. Across the life of Instagram, this produces a continual race against the effects of commercial interests; the online data is perpetually degraded by fake accounts, bots and other exploits which obscure users’ preference. So researchers using this data should approach it sceptically. In December 2014, an event widely dubbed the ‘Instagram Rapture’ saw millions of false accounts deleted by Instagram administrators. Rapper Akon reportedly lost more than half his followers overnight (Lee, 2014). But new problems persist, which have evaded the Instagram administrators. We have access to even less information than these administrators, and so identifying the mechanisms of data manipulation can be extremely difficult. What is relatively easy to identify are unexpected or unusual results; knowing how the data has been manipulated is not necessary to know the data has been manipulated and to discount it accordingly. Here we list a number of known Instagram exploits. We discuss how we can cursorily identify them and how they would impact our data.

Reciprocal following operations and automated engagement: Backwoods is a private gallery in Melbourne which exhibits Australian and some international street artists. Works are typically priced in the range of thousands to tens of thousands of dollars and new artists are exhibited monthly. On Instagram, the Backwoods_Gallery account has just over 100K followers. This makes the account larger than most street artists (larger than Swoon or D*Face) or any of the artists the gallery exhibits. The question arises: How does a relatively obscure Australian gallery collect more Instagram followers than most internationally recognised street artists? One clue is immediately revealed on their opening page: The account is following 50.4K other accounts. The median number of accounts to follow on Instagram is only 265, making Backwoods’ following list 190 times larger than the median. Of the 50.4K accounts Backwoods follow, 9.0K reciprocate the following (an 18 percent reciprocal follower rate). This only represents the current number of accounts Backwoods follows; the following list could have been cleared a number of times and historically @Backwoods_gallery may have followed many more.

Paid online services such as Likestagram or bots like Instagram Avenger or Instaget can automatically follow accounts posting images with specific hashtags, with the expectation of generating a reciprocal following. They can also engage with other accounts automatically (by spamming comments or likes) to boost exposure. Although these services breach Instagram’s terms of use they are not typically flagged by Instagram administrators until they exceed a threshold of use. Automated comments may not immediately be recognised by the Instagram administrators. In our analysis, this could affect the composition of an artist’s follower list; for example, if an artist account targeted users posting #streetart images over #bombing images, it would shift their position within the cultural map (Figure 2). This again emphasises that the cultural map is indicative of the audience following the artist (however it has been generated) not the content the artist produces.

By targeting a competitor’s follower list with a reciprocal following operation, companies can poach an audience from their competition. If this type of exploit was used between general street art accounts, it would artificially increase the correlation factor between the two accounts, which would lead us to underestimate the total audience size. We have no reason to suspect this has happened in our data analysis, but it remains a potential hazard.

Bot followers: The services discussed above populate an account with real people. However alternative services allow for the purchase of bot, or ‘robot’, followers, simply to boost the follower counter on the landing page (to make the account appear to have more engagement). Purchased bot followers offer minimal engagement and can lower the account edgerank score (although newer services also allow for the purchase of automated engagement, such as likes and comments). Bots can also be used for spam advertising; they continually comment only with a link to generate traffic to an external Web site.

We do a cursory search for high proportions of bot accounts in our audience lists: accounts that have low numbers of posts (or followers) or disproportionately high numbers of private accounts following a single account. But we are unlikely to be able to identify more advanced bots with this information and in fact cannot find any accounts within this study that may have purchased significant numbers of followers.

If an artist bought bot followers, how would this affect our result? The bot accounts are unlikely to follow other street art or graffiti bombing accounts, so we would expect it to dilute the audience with empty place holder accounts that don’t overlay with the street art or bombing audience lists. This would push the artist account towards the category of non-belonging; lower correlation factors with both street art and bombing. When estimating total audience size, bot accounts following a street art or bombing page would make the total audience appear larger; it would lead to an overestimate of the total audience size.

Conclusion

The continued growth of the number of Instagram users highlights that cultural engagement is increasingly mediated through mobile phone screens and image feeds. This affords the humanities researcher a vantage point to visualize the collective eye of the cultural consumer; organising the Instagram data stream becomes an incredibly powerful tool. We have used this tool to interrogate how audiences perceive ‘street art’ and ‘graffiti bombing’ genres, which both constitute types of public space inscriptive marking. But the analysis is equally applicable to any terminology or genres a cultural researcher wishes to compare.

We have mapped the preferences of an audience by tracking followers. But we could have equally mapped the images, by tracking the specific hashtags: When are images created? Where are images created? What words accompany the images? Academics have access to all this temporal, spatial and structural linguistic data and so research opportunities are extremely expansive.

Genres continually shift. This methodology allows us to map a transitory landscape; the classifications are not immutable but are inherently shifting over time. Within our Cartesian cultural map (Figures 2, 3 and 4), we can conceive of the artist accounts not within a scalar field (as a series of discrete fixed loci) but a vector field; each artist has a given location and a trajectory at any given time within the map. By continuing to log the data, we could conceivably chart an artist’s entire career path and also the broad cultural trends affecting all artists in the field.

In technical terms this method is easy to use, even for a researcher with limited mathematical or programming experience. We have specifically developed a straightforward analysis that can be performed in the widely used platform Microsoft Excel (or Python or other programs) with a series of simple commands. The online data scraping is performed externally by a commercial crawler service, Magi Metrics (http://www.magimetrics.com). Paired with an academic researcher who can interpret the results, we think the analysis methodology provides a very promising analytical tool.

About the authors

Dr. Christopher Honig is a practicing street artist and a lecturer in the Department of Chemical and Biomolecular Engineering in the Faculty of Engineering at the University of Melbourne.
E-mail: christopher [dot] honig [at] unimelb [dot] edu [dot] au

Dr. Lachlan Macdowall is an artist and cultural researcher in the Centre for Cultural Partnerships in the Faculty of the VCA and MCM, University of Melbourne.
E-mail: lmacd [at] unimelb [dot] edu [dot] au